A Comprehensive Study on Transformer-Based Time Series Forecasting

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 159 - 180

Опубликована: Янв. 17, 2025

Time series forecasting is crucial for various real-world applications, such as energy consumption, traffic flow estimation, and financial market analysis. This chapter explores the application of deep learning models, specifically transformer-based models long-term time forecasting. Despite success transformers in sequence modeling, their permutation-invariant nature can lead to loss temporal information, posing challenges accurate Especially, embedding position-wise vector or time-stamp key long Another noted headache standard model squared computation complexity. studies development research field timer forecasting, challenging pain point, popular data sets, state-of-the-art benchmarks. The discussion covers implications, limitations, future directions, offering insights applying these advanced techniques problems.

Язык: Английский

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Machine Learning for Financial Data Forecasting

Advances in finance, accounting, and economics book series, Год журнала: 2025, Номер unknown, С. 461 - 488

Опубликована: Янв. 22, 2025

Recent studies underscore the growing application of machine learning (ML) in finance, as revealed through bibliometric analyses. Kureljusic and Karger (2024) reviewed AI-based forecasting financial accounting, identifying gaps proposing future research agendas. Similarly, Biju et al. (2023) explored taxonomy AI, deep learning, ML highlighting rise publications need for empirical on algorithmic technologies. Building this foundation, study presents a scientometric analysis data from 1996 to 2024. Using Scopus Web Science, we examine key themes, collaboration networks, influential contributors. Employing tools like Sentence-BERT, BerTopic, BERT, ChatGPT, PEGASUS, offer insights into how has reshaped forecasting. This provides basis research, guiding scholars practitioners towards impactful areas finance.

Язык: Английский

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Deep Learning in Finance: A Survey of Applications and Techniques

Опубликована: Авг. 20, 2024

Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At core of this transformation is deep (DL), a subset ML that robust at processing analyzing complex large datasets. This paper provides concise overview key models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief (DBNs), Transformers, Generative Adversarial (GANs), Reinforcement Learning (Deep RL). The study examines their processes, mathematical foundations, practical in finance. It also explores recent advances emerging trends alongside critical challenges data quality, model interpretability, computational complexity, offering insights into future research directions can guide development more explainable models.

Язык: Английский

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A novel method for corn futures price prediction integrating decomposition, denoising, feature selection and hybrid networks

Annals of Operations Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 18, 2025

Язык: Английский

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Forecasting the Bitcoin price using the various Machine Learning: A systematic review in data-driven marketing

Systems and Soft Computing, Год журнала: 2025, Номер unknown, С. 200209 - 200209

Опубликована: Фев. 1, 2025

Язык: Английский

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TS-HTFA: Advancing Time-Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models

Symmetry, Год журнала: 2025, Номер 17(3), С. 401 - 401

Опубликована: Март 7, 2025

Given the significant potential of large language models (LLMs) in sequence modeling, emerging studies have begun applying them to time-series forecasting. Despite notable progress, existing methods still face two critical challenges: (1) their reliance on amounts paired text data, limiting model applicability, and (2) a substantial modality gap between time series, leading insufficient alignment suboptimal performance. This paper introduces Hierarchical Text-Free Alignment (TS-HTFA) novel method that leverages hierarchical fully exploit representation capacity LLMs for analysis while eliminating dependence data. Specifically, data are replaced with adaptive virtual based QR decomposition word embeddings learnable prompts. Furthermore, comprehensive cross-modal is established at three levels: input, feature, output, contributing enhanced semantic symmetry modalities. Extensive experiments multiple benchmarks demonstrate TS-HTFA achieves state-of-the-art performance, significantly improving prediction accuracy generalization.

Язык: Английский

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Stock Price Forecasting With Integration of Sectoral Behavior: A Deep Auto‐Optimized Multimodal Framework

Journal of Forecasting, Год журнала: 2025, Номер unknown

Опубликована: Март 16, 2025

ABSTRACT This study proposes a novel deep auto‐optimized architecture for stock price forecasting that integrates sectoral behavior with individual sentiment to improve predictive accuracy. Traditional prediction models often focus solely on behavior, overlooking the impact of broader trends. The proposed approach utilizes advanced learning models, including gated recurrent units (GRU), bidirectional GRU, long short‐term memory (LSTM), and LSTM, their hybrid ensembles. These are built using Keras functional API auto ML network search technology. current multimodal framework incorporates significantly improving performance metrics. research highlights critical role integrating in models.

Язык: Английский

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Zilean: A Modularized Framework for Large-scale Temporal Concept Drift Type Classification

Information Sciences, Год журнала: 2025, Номер unknown, С. 122134 - 122134

Опубликована: Март 1, 2025

Язык: Английский

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Multi-period Learning for Financial Time Series Forecasting

Опубликована: Апрель 4, 2025

Язык: Английский

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Evaluating the Inclusion of Technical Indicators for Deep Learning-Empowered Stock Price Prediction in the Chinese Market

Lecture notes in business information processing, Год журнала: 2025, Номер unknown, С. 3 - 17

Опубликована: Янв. 1, 2025

Язык: Английский

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